mlsanityData to 7 Oct 2026

How do Voices from Past Speech Synthesis Challenges Compare Today?

arXiv 2105.02373 ↗First posted 5 May 2021cs.SD eess.AScited by 21

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Abstract (arXiv)

Shared challenges provide a venue for comparing systems trained on common data using a standardized evaluation, and they also provide an invaluable resource for researchers when the data and evaluation results are publicly released. The Blizzard Challenge and Voice Conversion Challenge are two such challenges for text-to-speech synthesis and for speaker conversion, respectively, and their publicly-available system samples and listening test results comprise a historical record of state-of-the-art synthesis methods over the years. In this paper, we revisit these past challenges and conduct a large-scale listening test with samples from many challenges combined. Our aims are to analyze and compare opinions of a large number of systems together, to determine whether and how opinions change over time, and to collect a large-scale dataset of a diverse variety of synthetic samples and their ratings for further research. We found strong correlations challenge by challenge at the system level between the original results and our new listening test. We also observed the importance of the choice of speaker on synthesis quality.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2021.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 6; 2025: 5; 2026: 4.

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  1. 2024The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speechcited by 78
  2. 2025Audio Large Language Models Can Be Descriptive Speech Quality Evaluatorscited by 25
  3. 2021LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speechcited by 24
  4. 2022SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesiscited by 18
  5. 2024Enabling Auditory Large Language Models for Automatic Speech Quality Evaluationcited by 13
  6. 2024Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speechcited by 7
  7. 2024RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifiercited by 6
  8. 2025Flex-Judge: Text-Only Reasoning Unleashes Zero-Shot Multimodal Evaluatorscited by 6
  9. 2025From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modelingcited by 4
  10. 2022Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Featurescited by 2
  11. 2023Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selectioncited by 2
  12. 2026TTS-PRISM: A Perceptual Reasoning and Interpretable Speech Model for Fine-Grained Diagnosiscited by 2
9 more
  1. 2022Using Rater and System Metadata to Explain Variance in the VoiceMOS Challenge 2022 Datasetcited by 1
  2. 2024To what extent can ASV systems naturally defend against spoofing attacks?cited by 1
  3. 2026JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructionscited by 1
  4. 2022Comparison of Speech Representations for the MOS Prediction Systemcited by 0
  5. 2024Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challengecited by 0
  6. 2025Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillationcited by 0
  7. 2025Contrastive Learning for Semi-Supervised Deep Regression with Generalized Ordinal Rankings from Spectral Seriationcited by 0
  8. 2026Augmenting Dysarthric Speech Severity Assessment with MOS Supervisioncited by 0
  9. 2026XSQ-AST: An Explainable Audio Spectrogram Transformer Framework for Localising Synthetic Speech Artifactscited by 0